CO Adsorption Sites on Interstellar Water Ices Explored with Machine Learning Potentials

Data related to the calculations reported in the manuscript: "CO Adsorption Sites on Interstellar Water Ices Explored with Machine Learning Potentials. Binding energy distributions and snowline". Authors: G. M. Bovolenta, G. Molpeceres, K. Furuya, J. Kästner, and S. Vogt-Geisse. 

• XYZ Optimized structures CO+W(2-4) and CO+ASW stored in a compressed folder (.zip) 
• Binding energy (BE) data (.txt)
• Machine-learned potential (MLP) dataset, employed for the training of the MLP used to derive an accurate distribution of CO BE on ASW surfaces. The dataset is stored in python compressed array format (.npz) with the energies in kcal/mol and forces in kcal/mol/Ang. The dataset contains five numpy arrays: 

import numpy as np

data = np.load('CO_ASW_data_set.npz')
data['R']   # Cartesian coordinates of nuclei (Ang.)
data['E']   # Total energy (kcal/mol)
data['F']   # Forces (kcal/mol/Ang.)
data['N']   # Number of atoms in each structure
data['Z']   # Nuclear charges